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Python in Data Science: Intermediate Python

This course covers the essentials of using Python as a tool for data scientists to perform exploratory data analysis, complex visualizations, and large-scale distributed processing on “Big Data”. In this course, we cover essential mathematical and statistics libraries such as NumPy, Pandas, SciPy, SciKit-Learn, frameworks like TensorFlow and Spark, as well as visualization tools like matplotlib, PIL, and Seaborn. This course is ‘intermediate level’ as it assumes that attendees have solid data analytics and data science background and have basic Python knowledge.  Topics are introductory in nature but are covered in-depth, geared for experienced students.

This course is about 50% hands-on lab to 50% lecture ratio, combining engaging instructor presentations, demos, and practical group discussions with extensive machine-based student labs and project work. Throughout the course, students will learn to write Python scripts and apply them within a scientific framework working with the latest technologies listed on the agenda. This course provides indoctrination in the practical use of the umbrella of technologies that are on the leading edge of data science development. 

Available formats for this course
In-Person
Live Online
Corporate
Corporate Online
Duration
5 days/40 hours of instruction

Starting at: $2595

Get the full details on this course Download the .PDF Brochure

Part 1: Python for Data Science
1. Python Review (Optional)
•    Python Language
•    Essential Syntax
•    Lists, Sets, Dictionaries, and Comprehensions
•    Functions
•    Classes, Modules, and imports
•    Exceptions
2. iPython
•    iPython basics
•    Terminal and GUI shells
•    Creating and using notebooks
•    Saving and loading notebooks
•    Ad hoc data visualization
•    Web Notebooks (Jupyter)
3. numpy
•    numpy basics
•    Creating arrays
•    Indexing and slicing
•    Large number sets
•    Transforming data
•    Advanced tricks
4.scipy
•    What can scipy do?
•    Most useful functions
•    Curve fitting
•    Modeling
•    Data visualization
•    Statistics
5. A tour of scipy subpackages
•    Clustering
•    Physical and mathematical Constants
•    FFTs
•    Integral and differential solvers
•    Interpolation and smoothing
•    Input and Output
•    Linear Algebra
•    Image Processing
•    Distance Regression
•    Root-finding
•    Signal Processing
•    Sparse Matrices
•    Spatial data and algorithms
•    Statistical distributions and functions
•    C/C++ Integration
6. pandas
•    pandas overview
•    Dataframes
•    Reading and writing data
•    Data alignment and reshaping
•    Fancy indexing and slicing
•    Merging and joining data sets
7. matplotlib
•    Creating a basic plot
•    Commonly used plots
•    Ad hoc data visualization
•    Advanced usage
•    Exporting images
8. The Python Imaging Library (PIL)
•    PIL overview
•    Core image library
•    Image processing
•    Displaying images
9. seaborn
•    Seaborn overview
•    Bivariate and univariate plots
•    Visualizing Linear Regressions
•    Visualizing Data Matrices
•    Working with Time Series data
10. SciKit-Learn Machine Learning Essentials
•    SciKit overview
•    SciKit-Learn overview
•    Algorithms Overview
•    Classification, Regression, Clustering, and Dimensionality Reduction
•    SciKit Demo
11. TensorFlow Overview
•    TensorFlow overview
•    Keras
•    Getting Started with TensorFlow

Part 2: Python on Spark
1. PySpark Overview
•    Python and Spark
•    SciKit-Learn vs. Spark MLlib
•    Python at Scale
•    PySpark Demo
2. RDDs and DataFrames
•    DataFrames and Resilient Distributed Datasets (RDDs)
•    Partitions
•    Adding variables to a DataFrame
•    DataFrame Types
•    DataFrame Operations
•    Dependent vs. Independent variables
•    Map/Reduce with DataFrames
3. Spark SQL
•    Spark SQL Overview
•    Data stores: HDFS, Cassandra, HBase, Hive, and S3
•    Table Definitions
•    Queries
4. Spark MLib
•    MLib overview
•    MLib Algorithms Overview
•    Classification Algorithms
•    Regression Algorithms
•    Decision Trees and forests
•    Recommendation with ALS
•    Clustering Algorithms
•    Machine Learning Pipelines
•    Linear Algebra (SVD, PCA)
•    Statistics in MLib
5. Spark Streaming
•    Streaming overview
•    Integrating Spark SQL, MLlib, and Streaming

  • Experienced data analysts, developers, engineers or anyone tasked with utilizing Python for data analytics tasks. 
  • Attending students are required to have a background in basic Python development skills.

Python in Data Science: Intermediate Python Schedule

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Live, Online Training
Nov 2nd - 6th 10:00 am - 6:00 pm ET
$2595
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Live, Online Training
Dec 7th - 11th 10:00 am - 6:00 pm ET
$2595

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